Novel Technologies for Systems and Network Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 4045

Special Issue Editors


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Guest Editor
College of Computer and Cyber Security and the Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350117, China
Interests: network security; big data; wireless communications; IoT; intelligent signal processing

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Guest Editor
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Interests: data security; multimedia content security; information hiding and digital watermarking; artificial intelligence security; intelligent fault detection; digital forensics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Soochow University, Suzhou 215301, China
Interests: intelligent security and trust provision for internet of things (IoT) networks; IoT data analytics and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The conventional technologies for system and network security are facing amount of challenges in six-generation (6G) network. This is mainly due to the open broadcast nature of radio signal, the dramatically increased computation capability of devices, and the high connections among systems, devices, machines, and people. Hence, new systems and network security methods are extremely important for 6G network, especially in the quantum age.

Technologies like machine and deep learning, physically unclonable functions, blockchain, and optimization methods can be considered to enhance the security of systems and network, such as the Internet-of-Things (IoT), Internet-of-Vehicles (IoV), Unmanned Aerial Vehicle (UAV) communication, and Satellite Communications (SATCOM) systems, just to name a few. How to enhance the security of these systems, while guaranteeing their communications is an open issue. Moreover, how to overcome the challenges brought by the quantum computer is required to be carefully considered.

The overarching aim of this special issue (SI) is to bring together leading researchers in both academia and industry from diverse backgrounds to advance the systems and network security,  as well as to consider their potential practical applications. Suitable topics for this SI include, but are not limited to, the following areas:

  • Machine and deep learning algorithms for system and network security;
  • Blockchain technique for system and network security;
  • Physical layer security;
  • AI-driven data analysis;
  • Joint optimization of security and communication;
  • Data security and privacy;
  • Online social network security, privacy and trust;
  • Security of communication protocols;
  • Cloud computing and virtualization security;
  • Biometrics security and privacy;
  • Access control and authorization;
  • Fault diagnosis and fault tolerance;
  • Information dissemination and control;
  • Multimedia content security;
  • Information hiding and digital watermarking;
  • Artificial intelligence security;
  • Deepfakes and detection techniques;
  • Digital forensics.

Prof. Dr. Li Xu
Prof. Dr. Hui Tian
Prof. Dr. He Fang
Guest Editors

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Keywords

  • wireless communication and network security
  • AI
  • blockchain
  • optimization

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Published Papers (2 papers)

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Research

20 pages, 3060 KiB  
Article
A Study on Designing Cyber Training and Cyber Range to Effectively Respond to Cyber Threats
by Yongjoo Shin, Hyukjin Kwon, Jaeyeong Jeong and Dongkyoo Shin
Electronics 2024, 13(19), 3867; https://doi.org/10.3390/electronics13193867 - 29 Sep 2024
Viewed by 743
Abstract
As cyberattacks become increasingly sophisticated with advancements in information and communication technology, the impact of cyberspace threats is growing in both civilian and defense sectors. The utilization of cyber capabilities in operations is on the rise, prompting major nations to continuously enhance their [...] Read more.
As cyberattacks become increasingly sophisticated with advancements in information and communication technology, the impact of cyberspace threats is growing in both civilian and defense sectors. The utilization of cyber capabilities in operations is on the rise, prompting major nations to continuously enhance their cyber capabilities. This study aims to establish a systematic approach to cyber operations training and propose a framework for the development of cyber training. A hybrid cyber training system is designed as a plan for temporal and spatial integration to simultaneously combine simulation-based training with real-world target training. To develop this concept, a literature review was conducted, expert consultations were held, and data were collected and analyzed through visits to relevant organizations and units. Additionally, the fundamental components of cyber training were examined from environmental, scenario-based, and operational perspectives, leading to the presentation of a development direction for effective cyber training. This study is anticipated to enhance response capabilities to evolving cyber threats and attacks, improve cyber operational proficiency, and secure cyber power to achieve dominance in cyberspace. Full article
(This article belongs to the Special Issue Novel Technologies for Systems and Network Security)
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12 pages, 464 KiB  
Communication
FedSpy: A Secure Collaborative Speech Steganalysis Framework Based on Federated Learning
by Hui Tian, Huidong Wang, Hanyu Quan, Wojciech Mazurczyk and Chin-Chen Chang
Electronics 2023, 12(13), 2854; https://doi.org/10.3390/electronics12132854 - 28 Jun 2023
Viewed by 1564
Abstract
Deep learning brings the opportunity to achieve effective speech steganalysis in speech signals. However, the speech samples used to train speech steganalysis models (i.e., steganalyzers) are usually sensitive and distributed among different agencies, making it impractical to train an effective centralized steganalyzer. Therefore, [...] Read more.
Deep learning brings the opportunity to achieve effective speech steganalysis in speech signals. However, the speech samples used to train speech steganalysis models (i.e., steganalyzers) are usually sensitive and distributed among different agencies, making it impractical to train an effective centralized steganalyzer. Therefore, in this paper, we present an effective framework, named FedSpy, using federated learning, which enables multiple agencies to securely and jointly train the speech steganalysis models without sharing their speech samples. FedSpy is a flexible and extensible framework that can work effectively in conjunction with various deep-learning-based speech steganalysis methods. We evaluate the performance of FedSpy by detecting the most widely used Quantization Index Modulation-based speech steganography with three state-of-the-art deep-learning-based steganalysis methods representatively. The results show that FedSpy significantly outperforms the local steganalyzers and achieves good detection accuracy comparable to the centralized steganalyzer. Full article
(This article belongs to the Special Issue Novel Technologies for Systems and Network Security)
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